## :camel: llama-cli


llama-cli is a straightforward golang CLI interface for [llama.cpp](https://github.com/ggerganov/llama.cpp), providing a simple API and a command line interface that allows text generation using a GPT-based model like llama directly from the terminal.

## Container images

The `llama-cli` [container images](https://quay.io/repository/go-skynet/llama-cli?tab=tags&tag=latest) come preloaded with the [alpaca.cpp 7B](https://github.com/antimatter15/alpaca.cpp) model, enabling you to start making predictions immediately! To begin, run:

```
docker run -ti --rm quay.io/go-skynet/llama-cli:v0.2  --instruction "What's an alpaca?" --topk 10000
```

You will receive a response like the following:

```
An alpaca is a member of the South American Camelid family, which includes the llama, guanaco and vicuña. It is a domesticated species that originates from the Andes mountain range in South America. Alpacas are used in the textile industry for their fleece, which is much softer than wool. Alpacas are also used for meat, milk, and fiber.
```

## Basic usage

To use llama-cli, specify a pre-trained GPT-based model, an input text, and an instruction for text generation. llama-cli takes the following arguments when running from the CLI:

```
llama-cli --model <model_path> --instruction <instruction> [--input <input>] [--template <template_path>] [--tokens <num_tokens>] [--threads <num_threads>] [--temperature <temperature>] [--topp <top_p>] [--topk <top_k>]
```

| Parameter    | Environment Variable | Default Value | Description                            |
| ------------ | -------------------- | ------------- | -------------------------------------- |
| template     | TEMPLATE             |               | A file containing a template for output formatting (optional).  |
| instruction  | INSTRUCTION          |               | Input prompt text or instruction. "-" for STDIN.   |
| input        | INPUT                | -             | Path to text or "-" for STDIN.                    |
| model        | MODEL_PATH           |               | The path to the pre-trained GPT-based model.      |
| tokens       | TOKENS               | 128           | The maximum number of tokens to generate. |
| threads      | THREADS              | NumCPU()      | The number of threads to use for text generation. |
| temperature  | TEMPERATURE          | 0.95          | Sampling temperature for model output.  |
| top_p        | TOP_P                | 0.85          | The cumulative probability for top-p sampling. |
| top_k        | TOP_K                | 20            | The number of top-k tokens to consider for text generation.  |


Here's an example of using `llama-cli`:

```
llama-cli --model ~/ggml-alpaca-7b-q4.bin --instruction "What's an alpaca?"
```

This will generate text based on the given model and instruction.

## Advanced usage

`llama-cli` also provides an API for running text generation as a service. 

Example of starting the API with `docker`:

```bash
docker run -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:v0.2 api
```

And you'll see:
```
┌───────────────────────────────────────────────────┐ 
│                   Fiber v2.42.0                   │ 
│               http://127.0.0.1:8080               │ 
│       (bound on host 0.0.0.0 and port 8080)       │ 
│                                                   │ 
│ Handlers ............. 1  Processes ........... 1 │ 
│ Prefork ....... Disabled  PID ................. 1 │ 
└───────────────────────────────────────────────────┘ 
```

You can control the API server options with command line arguments:

```
llama-cli api --model <model_path> [--address <address>] [--threads <num_threads>]
```

The API takes takes the following:

| Parameter    | Environment Variable | Default Value | Description                            |
| ------------ | -------------------- | ------------- | -------------------------------------- |
| model        | MODEL_PATH           |               | The path to the pre-trained GPT-based model.      |
| threads      | THREADS              | CPU cores     | The number of threads to use for text generation. |
| address      | ADDRESS              | :8080         | The address and port to listen on. |


Once the server is running, you can make requests to it using HTTP. For example, to generate text based on an instruction, you can send a POST request to the `/predict` endpoint with the instruction as the request body:

```
curl --location --request POST 'http://localhost:8080/predict' --header 'Content-Type: application/json' --data-raw '{
    "text": "What is an alpaca?",
    "topP": 0.8,
    "topK": 50,
    "temperature": 0.7,
    "tokens": 100
}'
```

## Using other models

13B and 30B models are known to work:

### 13B

```
docker run --name model --entrypoint /models quay.io/go-skynet/models:ggml2-alpaca-13b-v0.2
docker cp model:/models/model.bin ./

# Use the model with llama-cli
docker run -v $PWD:/models -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:v0.2 api --model /models/model.bin
```

### 30B

```
docker run --name model --entrypoint /models quay.io/go-skynet/models:ggml2-alpaca-30b-v0.2
docker cp model:/models/model.bin ./

# Use the model with llama-cli
docker run -v $PWD:/models -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:v0.2 api --model /models/model.bin
```

### Golang client API

The `llama-cli` codebase has also a small client in go that can be used alongside with the api:

```golang
package main

import (
	"fmt"

	client "github.com/go-skynet/llama-cli/client"
)

func main() {

	cli := client.NewClient("http://ip:30007")

	out, err := cli.Predict("What's an alpaca?")
	if err != nil {
		panic(err)
	}

	fmt.Println(out)
}
```

### Kubernetes

You can run the API directly in Kubernetes:

```bash
kubectl apply -f https://raw.githubusercontent.com/go-skynet/llama-cli/master/kubernetes/deployment.yaml
```